Ecological Complexity最新文献

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors : they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of the rasterdiv R package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time.

Subterranean termites build underground tunnels for foraging. The obtained food is transported to the nest through these tunnels, and consumed to maintain the termite colony. In this process, termites can cause damage to wooden structures. To develop effective control strategies to reduce termite damage, it is important to know the sizes of the termite populations in the tunnels. In this study, we proposed a method for estimating the termite population size using the spatial statistic indices including fractal dimension (FD), local density (LD), and join count statistic (JCS) for the tunnel patterns. However, the method needs further improvement to be applied in field conditions. For the method, we generated 8,000 tunnel pattern images (1,000 images for each N) using an agent-based model based on experimental data. Here, N (= 3, 4, ..., 10) represents the number of termites participating in tunnel construction in the simulation. Subsequently, we calculated the FD, LD and JCS values of the tunnel pattern and trained and verified the k-nearest neighbors (KNN) algorithm, using 5,600 and 2,400 images, respectively. The population size (N) was estimated based on the FD, LD and JCS using the KNN algorithm. The estimated accuracy for all N was 60% to 97% in the range of k = 1 to 300. If the model for tunnel pattern generation includes heterogeneous environmental conditions, the proposed method could be used to effectively estimate the actual number of termite populations. Finally, we briefly discuss the challenges affecting our model, and how these could be overcome.

We investigate a tritrophic system whose cyclic dominance is modelled by the rock–paper–scissors game. We consider that organisms of one or two species are affected by movement limitations, which unbalances the cyclic spatial game. Performing stochastic simulations, we show that mobility unevenness controls the population dynamics. In the case of one slow species, the predominant species depends on the level of mobility restriction, with the slow species being preponderant if the mobility limitations are substantial. If two species face mobility limitations, our outcomes show that being higher dispersive does not constitute an advantage in terms of population growth. On the contrary, if organisms move with higher mobility, they expose themselves to enemies more frequently, being more vulnerable to being eliminated. Finally, our findings show that biodiversity benefits in regions where species are slowed. Biodiversity loss for high mobility organisms, common to cyclic systems, may be avoided with coexistence probability being higher for robust mobility limitations. Our results may help biologists understand the dynamics of unbalanced spatial systems where organisms’ dispersal is fundamental to biodiversity conservation.

We examine the nonlinear reaction–diffusion–advection equations to modeling of the predator–prey system under heterogeneous carrying capacity of the prey, and Holling type II functional response. When advection and diffusion fluxes are absent or small, we detect the discrepancy between the resource (carrying capacity) and species distributions. The large diffusion eliminates this effect. We propose a modification of the functional response coefficients to provide the correlation between species distribution and resource in both cases. The numerical simulation of several models both under small and moderate advection–diffusion fluxes is carried out.

While behavioral responses of individual organisms can be predicted with optimal foraging theory, the theory of how individual behavior feeds back to population and ecosystem dynamics has not been fully explored. Ecological models of trophic interactions incorporating behavior of entire populations commonly assume either that populations act as one when making decisions, that behavior is slowly varying or that non-linear effects are negligible in behavioral choices at the population scale. Here, we scale from individual optimal behavior to ecosystem structure in a classic tri-trophic chain where both prey and predators adapt their behavior in response to food availability and predation risk. Behavior is modeled as playing the field, with both consumers and predators behaving optimally at every instant basing their choices on the average population behavior. We establish uniqueness of the Nash equilibrium, and find it numerically. By modeling the interactions as playing the field, we can perform instantaneous optimization at the individual level while taking the entire population into account. We find that optimal behavior essentially removes the effect of top-down forcing at the population level, while drastically changing the behavior. Bottom-up forcing is found to increase populations at all trophic levels. These phenomena both appear to be driven by an emerging constant consumption rate, corresponding to a partial satiation. In addition, we find that a Type III functional response arises from a Type II response for both predators and consumers when their behavior follows the Nash equilibrium, showing that this is a general phenomenon. Our approach is general and computationally efficient and can be used to account for behavior in population dynamics with fast behavioral responses.

In 2012 Broom and Rychtar developed a new framework to consider the evolution of a population over a non-homogeneous underlying structure, where fitness depends upon multiplayer interactions amongst the individuals within the population played in groups of various sizes (including one). This included the independent model, and as a special case the territorial raider model, which has been considered in a series of subsequent papers. Here individuals are based upon the vertex of a graph but move to interact with their neighbours, sometimes meeting in large groups. The most important single property of such populations is the fixation probability, the probability of a single mutant completely replacing the existing population. In a recent paper we considered the fixation probability for the Birth Death Birth (BDB) dynamics for three games, a Public Goods game, the Hawk–Dove game and for fixed fitnesses for a large number of randomly generated graphs, in particular seeing if important underlying graph properties could be used as predictors. We found two good predictors, temperature and mean group size, but some interesting and unusual features for one type of graph, Barabasi–Albert graphs. In this paper we use a regression analysis to investigate (the usual) three alternative evolutionary dynamics (BDD, DBB, DBD) in addition to the original BDB. In particular, we find that the dynamics split into two pairs, BDB/DBD and BDD/DBB, each of which give essentially the same results and found a good fit to the data using a quadratic regression involving the above two variables. Further we find that temperature is the most important predictor for the Hawk–Dove game, whilst for the Public Goods game the group size also plays a key role, and is more important than the temperature for the BDD/DBB dynamics.

In mathematical modelling, several different functional forms can often be used to fit a data set equally well, especially if the data is sparse. In such cases, these mathematically different but similar looking functional forms are typically considered interchangeable. Recent work, however, shows that similar functional responses may nonetheless result in significantly different bifurcation points for the Rosenzweig–MacArthur predator–prey system. Since the bifurcation behaviours include destabilizing oscillations, predicting the occurrence of such behaviours is clearly important. Ecologically, different bifurcation behaviours mean that different predictions may be obtained from the models. These predictions can range from stable coexistence to the extinction of both species, so obtaining more accurate predictions is also clearly important for conservationists. Mathematically, this difference in bifurcation structure given similar functional responses is called structural sensitivity. We extend the existing work to find that the Leslie–Gower–May predator–prey system is also structurally sensitive to the functional response. Using the Rosenzweig–MacArthur and Leslie–Gower–May models, we then aim to determine if there is some way to obtain a functional description of data so that different functional responses yield the same bifurcation structure, i.e., we aim to describe data such that our model is not structurally sensitive. We first add stochasticity to the functional responses and find that better similarity of the resulting bifurcation structures is achieved. Then, we analyse the functional responses using two different methods to determine which part of each function contributes most to the observed bifurcation behaviour. We find that prey densities around the coexistence steady state are most important in defining the functional response. Lastly, we propose a procedure for ecologists and mathematical modellers to increase the accuracy of model predictions in predator–prey systems.

In order to meet the 2015 Paris Agreement for 1.5 °C global warming, per capita emissions need to come down to 2.9 tonnes by 2030. Food systems are known to be a significant source of an individual's carbon footprint and demand attention in sustainability management. The objective of this research is to conceptualise and define an intersection between contagion theory and socio-ecological systems models. This is achieved using a population dynamics model between two groups characterised by a distinct food regime: omnivores and vegans. The greenhouse gas emissions of each food regime is used to estimate the city's changing carbon foodprint as the food regimes shift by social contagion. Social contagion is identified as a catalyst for social tipping points, and emission pathways are explored with a variety of different contagion variables to test sensitivity towards a tipping point. The main finding is that the urban carbon foodprint can be reduced significantly with widespread adoption of veganism, but that the foodprint reaches a minimum at 1.97 tonnes CO₂-equivalent per capita. This demonstrates the need to embed food demand in urban climate governance such as nudging towards plant-based food alternatives. Nudging is discussed as a lever of ecological importance to social contagion. Lastly, socio-ecological contagion is defined as the interactions between social contagion and damage done to ecological systems to measure peer-to-peer spread of environmental stewardship agendas, such as the journey to Veganville.

The complex dynamics of a contagious disease in which populations experience horizontal and vertical transmissions, size variation, and virus mutations are of considerable practical and theoretical interest. We model such a system by dividing a population into three distinct groups: susceptibles ($S$), $C$-infected ($C$) and $F$-infected ($F$), based on the Susceptible-Infectious-Susceptible ($SIS$) model. Once the individuals in the $C$-infected group recover from the disease, they gain no permanent immunity. The virus can mutate in the group $C$. When it does, the individuals become members of the $F$-infected group. The mutated virus causes a lethal and incurable disease with a high mortality rate. We discuss the model for two cases. For the first case, all the newborns from infected mothers develop the disease shortly after their birth. For the second case, there exist equal transmission rates and the $C$-infected population is lifelong infectious. Our analysis shows that both systems have positive solutions, and the first model possesses four equilibrium points, the trivial one (extinction of the species), $C$-free equilibrium (extinction of the ancestor virus) and two endemic equilibria of different properties. We identify the net population growth rates of the susceptible and $C$-infected groups for the existence of the equilibria of the first model. We define the conditions of parameters for which species extinction and endemic equilibria are locally asymptotically stable. We observe that bifurcation occurs at the $C$-free equilibrium. For the second model, we find that there is only one endemic equilibrium and it is always locally asymptotically stable. We also determine the region for the net population growth rates of the susceptible and $F$-infected groups for the existence of the endemic equilibrium.